ZSL vs ZSD loss: Many traditional non-end-to-end trainable ZSR methods consider different aspects of regularization [1]}, transductive setting [2]}, metric learning [3]}, domain adaptation [4]} and class attribute association [5]} etc. Similarly, the end-to-end trainable ZSR methods [6]}, [7]} employ different non-linearity in feature and semantic pipeline. But, those traditional loss formulations need to be redesigned in ZSD case to be compatible for both classification and box detection losses.
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